






"""simple docstring"""
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class      UpperCamelCase_			(a_	):
	__magic_name__       =							'''sew-d'''





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[Any]=32      ,      lowerCAmelCase_   :					Dict=768      ,      lowerCAmelCase_   :					List[str]=12      ,      lowerCAmelCase_   :					int=12      ,      lowerCAmelCase_   :					int=3_072      ,      lowerCAmelCase_   :					List[Any]=2      ,      lowerCAmelCase_   :					Any=512      ,      lowerCAmelCase_   :					Optional[Any]=256      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					int=("p2c", "c2p")      ,      lowerCAmelCase_   :					List[Any]="layer_norm"      ,      lowerCAmelCase_   :					Optional[Any]="gelu_python"      ,      lowerCAmelCase_   :					Tuple=0.1      ,      lowerCAmelCase_   :					List[Any]=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					Dict=0.0      ,      lowerCAmelCase_   :					int=0.1      ,      lowerCAmelCase_   :					Dict=0.0_2      ,      lowerCAmelCase_   :					int=1e-7      ,      lowerCAmelCase_   :					Tuple=1e-5      ,      lowerCAmelCase_   :					Dict="group"      ,      lowerCAmelCase_   :					Tuple="gelu"      ,      lowerCAmelCase_   :					Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,      lowerCAmelCase_   :					int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,      lowerCAmelCase_   :					Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,      lowerCAmelCase_   :					Optional[int]=False      ,      lowerCAmelCase_   :					List[Any]=128      ,      lowerCAmelCase_   :					Any=16      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					Tuple=0.0_5      ,      lowerCAmelCase_   :					List[str]=10      ,      lowerCAmelCase_   :					List[Any]=2      ,      lowerCAmelCase_   :					Optional[int]=0.0      ,      lowerCAmelCase_   :					Any=10      ,      lowerCAmelCase_   :					Optional[Any]=0      ,      lowerCAmelCase_   :					Optional[Any]="mean"      ,      lowerCAmelCase_   :					Any=False      ,      lowerCAmelCase_   :					List[str]=False      ,      lowerCAmelCase_   :					List[Any]=256      ,      lowerCAmelCase_   :					Optional[Any]=0      ,      lowerCAmelCase_   :					Tuple=1      ,      lowerCAmelCase_   :					List[str]=2      ,      **lowerCAmelCase_   :					Optional[Any]      ,      )		->     List[Any]:
				super().__init__(**UpperCamelCase_      ,      pad_token_id=UpperCamelCase_      ,      bos_token_id=UpperCamelCase_      ,      eos_token_id=UpperCamelCase_		)
				UpperCAmelCase_	:					Tuple     	=   hidden_size
				UpperCAmelCase_	:					int     	=   feat_extract_norm
				UpperCAmelCase_	:					Tuple     	=   feat_extract_activation
				UpperCAmelCase_	:					int     	=   list(UpperCamelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   list(UpperCamelCase_		)
				UpperCAmelCase_	:					Any     	=   list(UpperCamelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   conv_bias
				UpperCAmelCase_	:					Tuple     	=   num_conv_pos_embeddings
				UpperCAmelCase_	:					Dict     	=   num_conv_pos_embedding_groups
				UpperCAmelCase_	:					Optional[int]     	=   len(self.conv_dim		)
				UpperCAmelCase_	:					List[str]     	=   num_hidden_layers
				UpperCAmelCase_	:					List[Any]     	=   intermediate_size
				UpperCAmelCase_	:					List[str]     	=   squeeze_factor
				UpperCAmelCase_	:					Optional[int]     	=   max_position_embeddings
				UpperCAmelCase_	:					List[str]     	=   position_buckets
				UpperCAmelCase_	:					int     	=   share_att_key
				UpperCAmelCase_	:					str     	=   relative_attention
				UpperCAmelCase_	:					str     	=   norm_rel_ebd
				UpperCAmelCase_	:					List[Any]     	=   list(UpperCamelCase_		)
				UpperCAmelCase_	:					str     	=   hidden_act
				UpperCAmelCase_	:					Any     	=   num_attention_heads
				UpperCAmelCase_	:					Any     	=   hidden_dropout
				UpperCAmelCase_	:					str     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Optional[int]     	=   feat_proj_dropout
				UpperCAmelCase_	:					int     	=   final_dropout
				UpperCAmelCase_	:					List[Any]     	=   layer_norm_eps
				UpperCAmelCase_	:					str     	=   feature_layer_norm_eps
				UpperCAmelCase_	:					Dict     	=   initializer_range
				UpperCAmelCase_	:					List[str]     	=   vocab_size

				if (
				    (len(self.conv_stride		) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
				    or (len(self.conv_dim		) != self.num_feat_extract_layers)
				):
							raise ValueError(
							    "Configuration for convolutional layers is incorrect."
							    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
							    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
							    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				UpperCAmelCase_	:					Tuple     	=   apply_spec_augment
				UpperCAmelCase_	:					str     	=   mask_time_prob
				UpperCAmelCase_	:					int     	=   mask_time_length
				UpperCAmelCase_	:					Union[str, Any]     	=   mask_time_min_masks
				UpperCAmelCase_	:					Union[str, Any]     	=   mask_feature_prob
				UpperCAmelCase_	:					int     	=   mask_feature_length
				UpperCAmelCase_	:					Optional[Any]     	=   mask_feature_min_masks

				# ctc loss
				UpperCAmelCase_	:					List[str]     	=   ctc_loss_reduction
				UpperCAmelCase_	:					Any     	=   ctc_zero_infinity

				# sequence classification
				UpperCAmelCase_	:					Any     	=   use_weighted_layer_sum
				UpperCAmelCase_	:					List[Any]     	=   classifier_proj_size





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Tuple:
				return functools.reduce(operator.mul      ,      self.conv_stride      ,      1		)











"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp